CO2 Sequestration Safety Monitoring by using Seismic Imaging and Double Difference Tomography
Bibliographic record
Abstract
Shale gas has become an increasingly important clean energy, which has been explored worldwide in recent decades. In the shale gas production, supercritical CO 2 acts as a fracturing fluid. For preventing any kinds of leakage of the injected supercritical CO 2 , it is essential to monitor the stability of its storage hundreds of kilometres beneath the Earth’s surface. Seismic tomography is an imaging technique that uses induced seismic waves to create three dimensional images of the subsurface. It is an effective monitoring method to evaluate the caprock integrity in the CO 2 sequestration storage (CCS). In this experimental research, a simulated uniaxial compressive load was applied on a granite sample to analyze the stress redistribution for long-term in-situ caprock integrity during CO 2 injection. The induced seismic waves were recorded, and seismic events were located according to the Geiger algorithm. The frequency of seismic events correlates with the caprock failure evolution. Based on the frequency of seismic events and the failure process, the seismic data is divided into four regimes to examine the failure evolution. Finally, the double difference tomography (TomoDD) algorithm using arrival time was adopted to recalculate to modify the locations of seismic events and velocity structure in each regime. The results indicate that the passive seismic system can map the caprock stress distribution and allow for imaging of the caprock integrity. TomoDD exhibits sound improvements to relocate seismic events both in relative and absolute locations as well as to characterize the local velocity structure. The study further reveals that seismic monitoring along with TomoDD could evaluate the caprock failure accurately in the CCS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".